Y. Rostami; S.S. Hosseini; R. Moghaddasi
Abstract
Introduction: In recent years, price transmission analysis either spatially or vertically among separated markets has increasingly been drawn by methods that account not only for common non-stationary but also, for nonlinear dynamics in co-integration relationship of price series. If the price transmission ...
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Introduction: In recent years, price transmission analysis either spatially or vertically among separated markets has increasingly been drawn by methods that account not only for common non-stationary but also, for nonlinear dynamics in co-integration relationship of price series. If the price transmission is asymmetric among the specific stages of the supply chain, the price changes will not be affected quickly at the production level through the processing and/or retail level. A positive (negative) price asymmetry occurs, when a decrease (increase) is not immediately transmitted in prices at the farm level; whereas, an increase (decrease) would influence final consumer rapidly. Asymmetric price transmission is crucial because it influences welfare negatively. Prices allow producers and consumers to decide synchronously and also, leave the doors open for scarce resources to be allocated influentially. The transition from a planned economy to a market one mostly causes price liberalization come into play. However, price liberalization not only improves resource allocation but also, brings about higher price instability in comparison with an administrative system with fixed prices.
Materials and Methods: Many popular modeling techniques used to analyze vertical price transmission were initially investigated by using variations of a model which was first developed by Wolffram (1971) and later modified by Houck (1977), that known as a traditional approach in price transmission studies. The response of the retail price (RP) to a shock in the farm price (FP) was calculated by estimating the following equation:
Where:
and lnPr is the log of retail price , lnPf is the log of farm price, are the increases and decreases of the price at the farm respectively. M1 and M2 are legs duration and is coefficient of increase or decrease on retail price.
Markov-switching vector error correction model:
The Markov-switching vector error correction model (MSVECM) is a special case of the general Markov-switching vector autoregressive model which was initially proposed by Hamilton (1989) for analyzing the U.S. business cycle. The applicability of this model is, however, not restricted to this specific research question. Consequently, it can be viewed as a general framework for analyzing time series with different regimes whenever the corresponding state variable is not observed. According to the state of the system, MSVECM with shifts in some of the parameters can be expected to be more appropriate in this setting:
here, pt = (pft ,pmt)’ is the vector of market prices for farm (superscript f) and retail (superscript r), respectively, denotes the vector of intercept terms, α is the vector of adjustment coefficients, β is the co-integrating (long-run equilibrium) vector, ∆ indicates first differences, and D1, D2, … , Dk are matrices of short-run coefficients. The vector contains the residual errors of the farm and the retail equations.
Results and Discussion:-Houck`s model: The estimated parameters of the final Houck`s model are presented in Table 2
Table 2-Houck model results (dependent variable: retail price)
Price
transmission
Test
result
Valed
test
long run coefficient
Price change
Dec. Inc.
Short run coefficient
Price change
Dec. Inc.
Variable
Asymmetry
H0 riject
14.24
0.89 0.99
0.61 0.73
producer price
Source: Research findings
Markov-switching vector error correction model:
The number of regimes and lags were determined according Akaike information criterion. Therefore, a model with two regimes with three lags has finally been chosen and estimated. Adjustment pace, residual standard errors and the resulting margin in the long-run relation (which may be calculated from the estimated coefficient for the regime-specific constant and the corresponding adjustment rate coefficient estimation) allowed a more detailed interpretation of the single regimes to be put. Two regime equations are as follows:
Regime 1 Regime 2
LRMPt = -0.46 + 1.08LPMPt (4) LRMPt = -0.05 + 1.09LPMPt (5)
stdv (0.12) (0.01) stdv (0.04) (0.004)
Here regime 2, points to data that relates to 2003 until the end of 2006 and also, 2013 to 2015; whereas regime 1 refers to the first of 2007 till end of 2012. Thus the type of relationship between two series depends on policy actions that government adopts during the period. In other words, one should consider different relations prevailing in different periods and this is the novelty of current study in comparison with previous researches.
Conclusion: This paper analyzed vertical market integration for Iranian fluid milk market over the years 2003-2015. We exploit Houck and MSVECM models in order to analyze market integration deploying 153 monthly observations from March 2003 to December 2015. The results of this paper corroborate a view, claiming retailers can exercise significant market power, as used to be evidenced by asymmetric price responses in Iranian fluid milk market. Due to the existence of positive price asymmetry in farm-retail price transmission, the retail prices would be inclining more quickly to increases in farm price than to decreases implying serious welfare losses to the consumers. This result is also consistent with the empirical evidence of a significant market power in the milk market.
